Accelerated commercial battery electrode-level degradation diagnosis via only 11-point charging segments
Yu Tian, Cheng Lin, Xiangfeng Meng, Xiao Yu, Hailong Li, Rui Xiong
Abstract
Accelerated and accurate degradation diagnosis is imperative for the management and reutilization of commercial lithium-ion batteries in the upcoming TWh era. This work proposes a framework combining both deep learning and physical modeling to extend traditional capacity degradation diagnosis to a rapid and accurate degradation diagnosis at the electrode level using only readily measurable charging current and voltage signals. Deep learning is used to rapidly and robustly predict polarization-free incremental capacity analysis (ICA) curves in minutes, which are traditionally obtained in a dozen hours, and the physical model is to quantitatively reveal the electrode-level degradation modes by decoupling them from the ICA curves. It is demonstrated that 11 points collected at any starting state-of-charge (SOC) in a minimum of 2.5 minutes are sufficient to predict reliable ICA curves with a mean root mean square error (RMSE) of 0.2774 Ah/V. Accordingly, batteries can be accurately elevated based on their degradation at both macro and electrode levels. Through transfer learning, such a method can also be adapted to different battery chemistries, emphasizing the enticing potential for rapid promotion of this work in battery degradation diagnosis. • A framework combining both deep learning and physical modeling to achieve a rapid and accurate degradation diagnosis at the electrode level. • A deep learning architecture is proposed to predict polarization-free ICA curves and can use 11-point charging segments starting from any SOC as input. • The proposed method shows good robustness under various influencing factors that could seriously affect the ICA curve. • Through transfer learning, the proposed method can be adapted to batteries with different chemistries and still show good reliability.